Associate Data Scientist – Modeling, Analytics & Pipelines

Aera Technology is a pioneer in the growing category of Decision Intelligence Platforms and a Leader in the Gartner® Magic Quadrant™ for 2026– the technology to digitize, augment, and automate decision-making processes with AI and machine learning. Through our AI decision automation platform, Aera Decision Cloud™, we are helping the best-known brands in the world make smarter, faster decisions. The need for decision intelligence has never been greater. The digitization of the economy has introduced a paradox: Companies today are awash in data, yet the velocity, volume, and complexity of that information is outpacing their ability to make decisions with the speed and precision that today’s business environment demands. Decision intelligence represents the critical shift — moving from people making decisions with machine support to machines making decisions guided by people. As the digital economy accelerates into an era defined by agentic AI, this shift has become a competitive necessity, enabling organizations to act faster, adapt with confidence, and unlock value once beyond reach. Aera Technology is at the forefront of this agentic revolution. Recognized by IDC as a leader in decision intelligence and cited by Gartner in its Market Guide for Decision Intelligence, we’re the trusted choice of global market leaders. Built on a comprehensive and composable platform, Aera empowers organizations to optimize and automate all types of decisions, across every business area. We are looking for an Associate Data Scientist who is exceptional at understanding data, extracting insights, and turning those insights into robust machine learning or statistical models. You will own the full modeling workflow from exploratory analysis and feature engineering through to building reproducible pipelines that can be deployed into production for our customers. Responsibilities: • Partner with product and domain experts to translate ambiguous business questions into clear analytical and modeling problems. • Perform deep exploratory data analysis (EDA) on complex, noisy, and often incomplete datasets; validate data quality, uncover patterns, and frame hypotheses. • Design and implement features, select appropriate modeling approaches (e.g., regression, tree-based models, time series, clustering), and benchmark alternatives. • Write clean, well-structured Python code and notebooks for interactive analysis, experimentation, and result communication. • Build end‑to‑end data and modeling pipelines (data extraction, preprocessing, feature generation, model training, evaluation, and inference) that are modular, testable, and reproducible. • Collaborate with engineering teams to productionize models on the Aera platform, including packaging, versioning, monitoring, and validation. • Communicate findings and recommendations clearly to both technical and non‑technical stakeholders using visualizations, written narratives, and live walkthroughs. About You: • Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field. • 1–3 years of experience in data science, data analysis, or ML engineering, or equivalent strong project/internship experience. • Strong proficiency in Python for data analysis: pandas, NumPy, and common scientific/ML libraries (e.g., scikit‑learn; familiarity with TensorFlow/PyTorch is a plus). • Solid experience with EDA and statistical analysis; comfortable working in notebooks to iteratively explore data and build intuition. • Good working knowledge of SQL and data manipulation; able to join, aggregate, and transform data from multiple sources. • Experience structuring code into reusable components, applying basic software engineering practices (version control, testing, documentation). • Strong analytical thinking and problem‑solving skills, with a bias for clear, data‑backed decision making. Nice to have : • Exposure to production ML/analytics systems (scheduling, APIs, CI/CD, monitoring). • Experience with cloud platforms (AWS, GCP, or Azure) and distributed data frameworks (e.g., Spark). • Domain experience in supply chain, logistics, or operations.